Pulsed-Mode Metalorganic Vapor-Phase Epitaxy of GaN on Graphene-Coated c-Sapphire for Freestanding GaN Thin Films.

We report the growth of high-quality GaN epitaxial thin films on graphene-coated c-sapphire substrates using pulsed-mode metalorganic vapor-phase epitaxy, together with the fabrication of freestanding GaN films by simple mechanical exfoliation for transferable light-emitting diodes (LEDs). High-quality GaN films grown on the graphene-coated sapphire substrates were easily lifted off by using thermal release tape and transferred onto foreign substrates. Furthermore, we revealed that the pulsed operation of ammonia flow during GaN growth was a critical factor for the fabrication of high-quality freestanding GaN films. These films, exhibiting excellent single crystallinity, were utilized to fabricate transferable GaN LEDs by heteroepitaxially growing InxGa1-xN/GaN multiple quantum wells and a p-GaN layer on the GaN films, showing their potential application in advanced optoelectronic devices.

Individual tooth detection and identification from dental panoramic X-ray images via point-wise localization and distance regularization.

Dental panoramic X-ray imaging is a popular diagnostic method owing to its very small dose of radiation. For an automated computer-aided diagnosis system in dental clinics, automatic detection and identification of individual teeth from panoramic X-ray images are critical prerequisites. In this study, we propose a point-wise tooth localization neural network by introducing a spatial distance regularization loss. The proposed network initially performs center point regression for all the anatomical teeth (i.e., 32 points), which automatically identifies each tooth. A novel distance regularization penalty is employed on the 32 points by considering L2 regularization loss of Laplacian on spatial distances. Subsequently, teeth boxes are individually localized using a multitask neural network on a patch basis. A multitask offset training is employed on the final output to improve the localization accuracy. Our method successfully localizes not only the existing teeth but also missing teeth; consequently, highly accurate detection and identification are achieved. The experimental results demonstrate that the proposed algorithm outperforms state-of-the-art approaches by increasing the average precision of teeth detection by 15.71 % compared to the best performing method. The accuracy of identification achieved a precision of 0.997 and recall value of 0.972. Moreover, the proposed network does not require any additional identification algorithm owing to the preceding regression of the fixed 32 points regardless of the existence of the teeth.

Thermodynamically driven self-formation of Ag nanoparticles in Zn-embedded carbon nanofibers for efficient electrochemical CO2 reduction.

The electrochemical CO2 reduction reaction (CO2RR), which converts CO2 into value-added feedstocks and renewable fuels, has been increasingly studied as a next-generation energy and environmental solution. Here, we report that single-atom metal sites distributed around active materials can enhance the CO2RR performance by controlling the Lewis acidity-based local CO2 concentration. By utilizing the oxidation Gibbs free energy difference between silver (Ag), zinc (Zn), and carbon (C), we can produce Ag nanoparticle-embedded carbon nanofibers (CNFs) where Zn is atomically dispersed by a one-pot, self-forming thermal calcination process. The CO2RR performance of AgZn-CNF was investigated by a flow cell with a gas diffusion electrode (GDE). Compared to Ag-CNFs without Zn species (53% at -0.85 V vs. RHE), the faradaic efficiency (FE) of carbon monoxide (CO) was approximately 20% higher in AgZn-CNF (75% at -0.82 V vs. RHE) with 1 M KOH electrolyte.

Pose-aware instance segmentation framework from cone beam CT images for tooth segmentation.

Individual tooth segmentation from cone beam computed tomography (CBCT) images is an essential prerequisite for an anatomical understanding of orthodontic structures in several applications, such as tooth reformation planning and implant guide simulations. However, the presence of severe metal artifacts in CBCT images hinders the accurate segmentation of each individual tooth. In this study, we propose a neural network for pixel-wise labeling to exploit an instance segmentation framework that is robust to metal artifacts. Our method comprises of three steps: 1) image cropping and realignment by pose regressions, 2) metal-robust individual tooth detection, and 3) segmentation. We first extract the alignment information of the patient by pose regression neural networks to attain a volume-of-interest (VOI) region and realign the input image, which reduces the inter-overlapping area between tooth bounding boxes. Then, individual tooth regions are localized within a VOI realigned image using a convolutional detector. We improved the accuracy of the detector by employing non-maximum suppression and multiclass classification metrics in the region proposal network. Finally, we apply a convolutional neural network (CNN) to perform individual tooth segmentation by converting the pixel-wise labeling task to a distance regression task. Metal-intensive image augmentation is also employed for a robust segmentation of metal artifacts. The result shows that our proposed method outperforms other state-of-the-art methods, especially for teeth with metal artifacts. Our method demonstrated 5.68% and 30.30% better accuracy in the F1 score and aggregated Jaccard index, respectively, when compared to the best performing state-of-the-art algorithms. The major implication of the proposed method is two-fold: 1) an introduction of pose-aware VOI realignment followed by a robust tooth detection and 2) a metal-robust CNN framework for accurate tooth segmentation.

Field Investigation of Clay Balls in Full-Depth Asphalt Pavement.

Clay ball is a pavement surface defect which refers to a clump in which clay or dirt is mixed with hot asphalt mixture. Clay ball is typically caused by a combination of aggregate contamination of clay or soil, high aggregate moisture, and low production temperature at the asphalt plant. It usually appears a few weeks or months after paving under traffic load, after being liquefied and knocked from the pavement surface. Clay balls can be the source of potholing, raveling, and other issues such as moisture infiltration and reduced ride quality. This paper presents an investigation of the clay balls on US-31 one winter after construction in Hamilton County, Indiana. In order to understand the pavement condition, their severity was measured using both visual observation and infrared image collection system. In addition, a clay ball amount, its distribution pattern, and cores condition were evaluated. A precipitation effect on clay ball formation was investigated for finding a cause of the clay balls. The investigation found that infrared image collection system was appropriate in detecting the clay balls. The clay balls were elliptic in shape with 2.5 cm to 10 cm in diameter, and the maximum clay ball depth was almost penetrating the entire surface course. It was also found that the asphalt paving on the raining days or right after raining could increase the potential of clay balls. Monitoring of aggregate moisture during construction on or after raining days should be able to reduce the risk of clay balls.

Effects of the Computer Desk Level on the Musculoskeletal Discomfort of Neck and Upper Extremities and EMG Activities in Patients with Spinal Cord Injuries.

BACKGROUND: Computers are used as a means of social communication, for work and other purposes. However, patients with spinal cord injuries may have a higher risk than normal individuals with musculoskeletal problems when using computers owing to their inability to control respective postures due to problems in motor and sensory functioning. OBJECTIVES: This study is aimed at identifying the effect of computer desk heights on musculoskeletal discomforts of the neck and upper extremities and EMG activities in patients with spinal cord (C6) and upper thoracic spinal cord injuries. METHODS: Participants of the present study were the patients diagnosed with ASIA A or B. The patients were divided into two groups according to their spinal cord injuries: C6 group and T2-T6 group. The level of the desk was set at 5 cm below the elbow, at the elbow level, and 5 cm above the elbow level. Electromyography was used to measure the duration of typing task EMG(%RVC) of the cervical erector spinae, upper trapezius, anterior deltoid, and wrist extensor. Subjective musculoskeletal discomfort (Borg-RPE) was measured at the end of the experiment. RESULTS: The two groups showed differences in terms of RPE corresponding to each level of the computer desk (p < .05). Postanalysis revealed the C6 group had decreased RPE as the level of computer desk increased, whereas the subjects in the T2-T6 group had decreased RPE values in accordance with the decreasing level of computer desk (p < .05). In EMG, both groups had no significant differences (p > .05). However, in terms of the interaction between the muscles and the level of computer desk in both groups, the differences in the interactions of the upper trapezius and wrist extensor with each level of the desk were found (p < .05). CONCLUSION: This study is meaningful in that it confirms computer work posture and preference of spinal cord-injured individuals.

Prioritizing biological pathways by recognizing context in time-series gene expression data.

BACKGROUND: The primary goal of pathway analysis using transcriptome data is to find significantly perturbed pathways. However, pathway analysis is not always successful in identifying pathways that are truly relevant to the context under study. A major reason for this difficulty is that a single gene is involved in multiple pathways. In the KEGG pathway database, there are 146 genes, each of which is involved in more than 20 pathways. Thus activation of even a single gene will result in activation of many pathways. This complex relationship often makes the pathway analysis very difficult. While we need much more powerful pathway analysis methods, a readily available alternative way is to incorporate the literature information. RESULTS: In this study, we propose a novel approach for prioritizing pathways by combining results from both pathway analysis tools and literature information. The basic idea is as follows. Whenever there are enough articles that provide evidence on which pathways are relevant to the context, we can be assured that the pathways are indeed related to the context, which is termed as relevance in this paper. However, if there are few or no articles reported, then we should rely on the results from the pathway analysis tools, which is termed as significance in this paper. We realized this concept as an algorithm by introducing Context Score and Impact Score and then combining the two into a single score. Our method ranked truly relevant pathways significantly higher than existing pathway analysis tools in experiments with two data sets. CONCLUSIONS: Our novel framework was implemented as ContextTRAP by utilizing two existing tools, TRAP and BEST. ContextTRAP will be a useful tool for the pathway based analysis of gene expression data since the user can specify the context of the biological experiment in a set of keywords. The web version of ContextTRAP is available at http://biohealth.snu.ac.kr/software/contextTRAP .

Effects of task-oriented treadmill-walking training on walking ability of stoke patients.

BACKGROUND: Generally, treadmill-walking training focuses on weight bearing and the speed of walking. However, changes in direction, speed, and slope while walking require adaptation. OBJECTIVE: The effects of task-oriented treadmill-walking training (TOTWT) on the walking ability of stroke patients were evaluated. METHODS: Subjects were randomly divided into two groups: the task-oriented treadmill-walking training (TOTWT) group and the conventional treadmill-walking training (CTWT) group. Evaluation was performed before the commencement of the training and again 4 and 8 wk after training was initiated. The OptoGait system measured gait parameters. The Timed Up and Go test and 6-min walk test were also performed. RESULTS: Within each group, both the TOTWT and the CTWT groups significantly differed before and after the intervention in all tests (P < 0.05); the CTWT group showed greater improvement in all tests following TOTWT (P < 0.05). CONCLUSION: TOTWT improves gait and rehabilitation in the stroke-affected limb, and also improves general gait characteristics.

Effects of stabilization exercise using a ball on mutifidus cross-sectional area in patients with chronic low back pain.

The purpose of this study was to compare the effects of lumbar stabilization exercises using balls to the effects of general lumbar stabilization exercises with respect to changes in the cross section of the multifidus (MF), weight bearing, pain, and functional disorders in patients with non-specific chronic low back pain. Twelve patients participated in either a 8 week (3 days per week) stabilization exercise program using balls and control group (n = 12). The computer tomography (CT) was used to analyze MF cross-sectional areas (CSA) and Tetrax balancing scale was used to analyze left and right weight bearing differences. Both groups had significant changes in the CSA of the MF by segment after training (p < 0.05) and the experimental group showed greater increases at the L4 (F = 9.854, p = 0.005) and L5 (F = 39. 266, p = 0.000). Both groups showed significant decreases in weight bearing, from 9.25% to 5.83% in the experimental group and from 9.33% to 4.25% in the control group (p < 0.05), but did not differ significantly between the two groups. These results suggests that stabilization exercises using ball can increases in the CSA of the MF segments, improvement in weight bearing, pain relief, and recovery from functional disorders, and the increases in the CSA of the MF of the L4 and L5 segments for patients with low back pain. Key PointsCompared with the stabilization exercise using a ball and general stabilization exercise increased the CSA of the MF, weight bearing, pain, and functional ability in patients with low back pain.We verified that increases in the CSA of the MF of the L4 and L5 segments and functional ability during the stabilization exercise using a ball.The stabilization exercise using a ball was shown to be an effective exercise method for patients with low back pain in a rehabilitation program by increasing functional ability and the CSA of the MF.

Down-regulation of delayed rectifier K+ channels in the hippocampus of seizure sensitive gerbils.

In order to confirm the species-specific distribution of voltage-gated K(+) (Kv) channels and the definitive relationship between their immunoreactivities and seizure activity, we investigated Kv2.x, Kv3.x and Kv4.x channel immunoreactivities in the hippocampi of seizure-resistant (SR) and seizure-sensitive (SS) gerbils. There was no difference in Kv2.1, Kv3.4, Kv4.2 and Kv4.3 immunoreactivity in the hippocampus between SR and SS gerbils. In comparison to SR gerbils, Kv3.1b immunoreactivity in neurons was significantly lower in SS gerbils instead Kv3.1b-immunoreactive astrocytes were clearly observed in SS gerbils (p<0.05). Kv3.2 immunoreactivity was also significantly lower in neurons of SS gerbils than in those of SR gerbils (p<0.05). Considering the findings of our previous study, these findings suggest that delayed rectifier K(+) channels (Kv1.1, Kv1.2, Kv1.5, Kv1.6, Kv2.1 and Kv3.1-2), not A-type K(+) channels (Kv1.4, Kv3.4 and Kv4.x), may be down-regulated in the SS gerbil hippocampus, as compared to SR gerbils.

A cube framework for incorporating inter-gene information into biological data mining.

Large volumes of microarray data are registered daily in public repositories such as SMD (Belkin and Niyogi, 2003) and GEO (Ashburner et al., 2000). Such repositories have quickly become a community resource. However, due to the inherent heterogeneity of the microarray experiments, the data generated from different experiments could not be directly integrated and hence the resources have not been fully utilised. To address this problem, we propose a new microarray integration framework that achieves high-quality integration through exploiting invariant features such as relative information among genes. We also show how the proposed approach generalises the previous frameworks.